PenalisedLikelihood.beta

PenalisedLikelihood

PenalisedLikelihood.default

Used to find suitable starting parameters for nonconjugate mixtures.
For some mixing distributions this hasn't been implemented yet.

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the 'dirichletprocess' package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

Dean Markwick

dirichletprocess

Build Dirichlet Process Objects for Bayesian Modelling

Gordon J. Ross

Kees Mulder

Giovanni Sighinolfi

Filippo Fiocchi 

PenalisedLikelihood.beta function

<dl><dt>mdObj</dt>
<dd>Mixing distribution object</dd>
<dt>x</dt>
<dd>Data</dd></dl>

Arguments

Calculate the parameters that maximise the penalised likelihood. — PenalisedLikelihood.beta

<dl>

<dt>mdObj</dt>
<dd>Mixing distribution object</dd>


<dt>x</dt>
<dd>Data</dd>

</dl>

PenalisedLikelihood.beta: Calculate the parameters that maximise the penalised likelihood.

Description

Usage

Arguments